We apply the proposed approach to the case study of medical liability using a GAN architecture. The goal is to investigate the effect of generalizability on the contract's risk exposure using synthetic samples while controlling the degree of similarity to the original dataset. The GAN network was built leveraging the GAN-Sandbox package and was implemented in Python using the Keras library with a Tensorflow backend [1]}, [2]}, [3]}.
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